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61 to 70 of 2,527 Results
Adobe PDF - 763.2 KB - MD5: 8ee193af01c4ea867ea0c5c89828a8f9
Study Protocol
Adobe PDF - 2.4 MB - MD5: 8ad99218bf0d82a2bcd5bfc3d2053fc1
DocumentationVariable Codebook/Data Dictionary
Plain Text - 7.0 KB - MD5: 71d0d749bde7b73b30ceb76f3f53e741
DocumentationReadme
May 8, 2025 - Biosciences Dataverse
Okoko, Miguel; Karisa, Jonathan; Gona, Rehema; Odongo, Tobias; Otieno, Bruno; Yaa, Festus; Meli, Peter; Ondieki, Zedekiah; Tuwei, Mercy; Bartilol, Brian; Shee, Faiz; Chaccour,Carlos; Wanjiku, Caroline; Kiuru, Caroline; Maia, Marta F., 2025, "Replication Data for: Phenotypic and genotypic insecticide resistance profiles of main malaria vectors in Kwale county, Kenya", https://doi.org/10.7910/DVN/ZJNGZR, Harvard Dataverse, V3, UNF:6:mvCQj64WTTmzp/zEz4vwDA== [fileUNF]
This is a replication dataset for the manuscript titled: "Phenotypic and genotypic insecticide resistance profiles of main malaria vectors in Kwale county, Kenya". Long-lasting insecticide-treated nets are the primary malaria vector control measure in coastal Kenya. In 2018, phenotypic resistance to pyrethroids and low frequency of L1014S kdr mutat...
May 6, 2025 - Biosciences Dataverse
Mwangi, Shaban; Gomes, Claudia; Abdi, Abdirahman, 2025, "Replication Data for: Specific Biomarkers Differentiate Cerebral Malaria from Other Causes of Coma in African children", https://doi.org/10.7910/DVN/7IKHOR, Harvard Dataverse, V1, UNF:6:KLAMdC6vofVKSpF4BYxeAA== [fileUNF]
This is a replication dataset for the manuscript titled: "Specific Biomarkers Differentiate Cerebral Malaria from Other Causes of Coma in African children" The dataset file contains study participants' clinical characteristics and measurements as well as measured/quantified ELISA analytes.
Tabular Data - 46.3 KB - 29 Variables, 380 Observations - UNF:6:KLAMdC6vofVKSpF4BYxeAA==
csv fileData
May 6, 2025 - Health Economics Research Unit (HERU) Dataverse
Mugenya, Irene; Mbogori, Maurice Murugu; Oluoch, Dorothy; Amboko, Beatrice; Kosgei, Rose J.; Karimi, Janette; Nzinga, Jacinta, 2025, "Building the Evidence Base for Health System Transformation Through Designing and Testing of Future Health System Models and Measure: PNC Data", https://doi.org/10.7910/DVN/2P8DPM, Harvard Dataverse, V1, UNF:6:NZp9At7rhRTvhWYDWOU7Ow== [fileUNF]
Quality postnatal care (PNC) is crucial for safeguarding the health of both the mother and her newborn through effective screening, timely identification of complications, and adequate education on self-care and infant care. Timely interventions during this period have been shown to improve maternal and neonatal outcomes. However, evidence indicate...
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